code: def preprocess(self, data, vocab, pretrain_vocab, args):
      processed = []
      for sent in data:
          processed_sent = DataSample(
              word=[vocab["word"].map([w[0] for w in sent])],
              char=[
                  [vocab["char"].map([x for x in w[0]]) for w in sent]
              ],
              upos=[vocab["upos"].map([w[1] for w in sent])],
              xpos=[vocab["xpos"].map([w[2] for w in sent])],
              feats=[vocab["feats"].map([w[3] for w in sent])],
              pretrain=(
                  [pretrain_vocab.map([w[0].lower() for w in sent])]
                  if pretrain_vocab is not None
                  else [[PAD_ID] * len(sent)]
              ),
              text=[w[0] for w in sent],
          )
          processed.append(processed_sent)
  
      return processed
  
imports: import random
  import logging
  import copy
  import torch
  from collections import namedtuple
  from torch.utils.data import DataLoader
  from torch.nn.utils.rnn import pad_sequence
  from stanza.models.common.bert_embedding import filter_data
  from stanza.models.common.data import map_to_ids, get_long_tensor, get_float_tensor, sort_all
  from stanza.models.common.vocab import PAD_ID, VOCAB_PREFIX, CharVocab
  from stanza.models.pos.vocab import WordVocab, XPOSVocab, FeatureVocab, MultiVocab
  from stanza.models.pos.xpos_vocab_factory import xpos_vocab_factory
  from stanza.models.common.doc import *
label_desc: Use image node_id 3 for calling the Dataset obj's underlying member method code with example usage: obj.preprocess(data, vocab, pretrain_vocab, args) and returns: processed